Measurement: Making Sense of Metrics

Seeking to create a compelling work experience? Use big data and thick data for better outcomes.

Historically, the metric that mattered most when measuring the effectiveness of a workplace was square feet per person. Real estate and facilities professionals justified proposed workplace changes largely on how convincingly they promised to save money per square foot.

If any other consideration came into play, it was most likely the opinion of a HiPPO—the highest-paid person in an organization. Senior leaders who advocated for change—even if based on little more than a gut feeling— usually got their way.

Today, the bar is higher. Workplaces are expected to contribute to organizational effectiveness by influencing things like culture, collaboration, engagement and productivity, all while continuing to demonstrate efficient use of every square foot.

With demands so daunting—not to mention the investment required for significant workplace change— organizations are seeking to reduce the risks of big decisions by leveraging evidence-based data. Fortunately, technology has made unprecedented amounts of data available. The challenge now is to harness this data to inform decisions that can lead to greater performance of people and place.

Measuring the work experience

In the past, measurement was most effectively applied to tracking output on the factory floor. But today’s technology allows metrics to permeate the office environment, with many efforts underway to explore how evidence-based research can help shape a work culture where people are fulfilled and productive.

Today, data is so dominant it is possible to measure just about every aspect of the white-collar experience—and the workplace’s contribution to it.

In some cases, white-collar measurement is tangible. Gathering data regarding market share and other aspects of financial performance, for instance, is straightforward. Many aspects of the white-collar experience, however, seem a bit nebulous—things like engagement, satisfaction, wellbeing and employees feeling valued. All of these, however, can be measured, often via methods like employee surveys, focus groups, interviews and observation.

Data for workplace decisions

For employees, a big part of the work experience is the workplace. Consequently, researchers are developing and honing workplace metrics, often with the aid of technology, to better understand how a workplace is being used and how it might be improved.

The advent of occupancy sensors and badging systems, in particular, unleashed a torrent of data space planners can analyze to craft workplaces that use square footage efficiently and productively. This data provides insight into how physical space can be designed to support teamwork, provide privacy, boost collaboration, encourage healthy movement or rightsize conference rooms—whatever contributes to organizational goals and employee effectiveness.

Though workplace metrics can help optimize an organization’s investment in real estate, a more compelling purpose is to make real estate optimal for the people using it. After all, personnel expenses account for about 80% of a company’s budget on average, whereas real estate requires a tenth of that. The real value of workplace measurement lies in how it can be used to support people—an organization’s most important asset—not just place.

Big data, thick data

For measurement to merit reliable influence over workplace design, it needs to be both big and thick. Big data is quantitative— voluminous statistics which allow for deep processing or advanced analytical approaches. Thick data is qualitative—contextual insights providing greater depth than numbers alone.

Including measurement methodologies from both categories is important. There is a tendency today to favor quantitative data because technology has made it so readily available. The danger is more data does not necessarily equate to greater insight. More sources, however, often do.

By using a variety of big and thick data sources—both quantitative and qualitative—space planners can more effectively draw conclusions about how a workplace can best support its occupants. Big data shows what is happening, thick data tells why it is happening, and the juxtaposition of both provides insight into so what—not just what is going on, but what to do about it.

Case in point: Steelcase once conducted a sensor study that uncovered extremely low occupancy in a team area at a client organization. The client’s facilities manager immediately wanted to repurpose the space. First, though, Steelcase workplace experts examined employee surveys and found that employees would love to use the space but didn’t feel comfortable doing so because of a perceived lack of privacy. A few tweaks were all it took to reconcile the what with the why— the quantitative with the qualitative— thereby making the space more functional.

Transparency is key

Whatever methods you employ, be sure to let employees know what, when and why you are measuring. You don’t want to make people uncomfortable by coming across as Big Brother. If you undertake observation (also known as ethnography), let people know they are being observed. If you deploy sensors, be upfront about how you plan to use the data collected and who will have access to it.

The key is to portray measurement as a win-win. Communicate the data will not only help your organization achieve better results, but also provide value to employees by leading toward a more fulfilling work experience or more supportive workplace.

A step-by-step strategy

With so many measurement methods available to gain insight into the work experience, it can be tough to determine which will be most beneficial. The following is a step-by-step strategy for deploying a measurement program that unlocks greater value from your real estate and facilities.

  1. Know what matters most. What are the primary goals for your organization—productivity, engagement, retention, wellbeing? To get the best return on investment, focus on measuring those things that can have an impact on your key business drivers. Example: If collaboration is a high priority, you will want to dig into usage patterns in shared meeting areas, seeking clues that can help you fine-tune size, configuration, location and amenities.
  2. Determine what behaviors you want. For each business driver identified in step one, determine which behavioral attributes will be evident when it takes hold. What will you see people do and hear them say? Example: In a collaborative culture, you will see regular usage of informal meeting areas and hear people talking about key work topics or customer needs during chance encounters.
  3. Determine how to measure desired behaviors. Consider both quantitative and qualitative methods. Example: To quantify usage of informal meeting areas, occupancy sensors are especially effective. Spontaneous interaction, however, might be measured via qualitative approaches, including surveys, observation and interviews. Also consider both leading and lagging measurements. For example, a leading metric for safety is the number of workers wearing hard hats, while a lagging metric is the number of accidents. In the workplace, a leading indicator for collaboration might be the number of spaces that support people working together, while a lagging indicator would be people’s feedback about the extent to which they feel the space supports collaboration.
  4. Prioritize measurement methods. Evaluate potential measurements identified in step three based on the effort they would take and the impact they would have. Interviews are easy to conduct but probably will have limited impact on a company that prefers data-driven decisions. Sensor studies require more effort, but can make a compelling, quantitative case for change.
  5. Don’t overdo it. Seek to measure as much as necessary but as little as possible. You want to gather just enough information to demonstrate results and inform improvement. Avoid devoting resources to collecting data that will never be used.

illustration of a man and woman standing on a chart while they are writing and touching colored charts.

Extracting value from measurement

True Story: Year after year, an HR exec compiled a mountain of data into a report that scrutinized personnel matters from every angle. Year after year, she slipped an offer into page 28: “If you’ve read this far, contact me and I’ll send you $5.”

Only one person ever did. The moral of the story: Collecting data simply for the sake of collecting data is pointless. It’s bad enough that hardly anyone read the report. Even worse, far too much work went into compiling data that simply died—no one was using it to make decisions.

Without interpretation—analysis that makes sense of measurement—data is a burden. Collecting even more data won’t help; it just becomes an even bigger burden. After all, finding a needle of insight in a haystack of data won’t get easier if the haystack keeps getting bigger.

For measurement to be effective, you must distinguish the signal from the noise. Think of data as a lighthouse in which the beacon is the signal and the surrounding fog represents the noise. Providing a significant amount of data won’t do much good because it just looks like noise. If you first make sense of the data, however, provide insights— the signal—you’ll have a compelling business case to take decisive action toward a workplace that improves the work experience.

Hierarchy of data value

The pyramid below represents the hierarchy of data value. Data itself is the foundation, whereas insight— what you need to know to take action—resides at the top.

To climb the pyramid, you need to add meaning to the data to get information. Next, you interpret the information and apply it to your organization to get insight.

Here is an example of how this progression might work:

Level 1: Examine the data. An examination of sensor data reveals that, on average, private offices are occupied just 20% of the time.

Level 2: Add meaning to get information. The fact that executives travel a lot gives the data meaning. This context gives you information: Considering executive travel demands, it is reasonable to assume many private offices will often be underused.

Level 3: Apply information to get actionable insight. Eliminating executive offices may not be a realistic solution—but here’s an alternative that might work: Employees frequently complain about difficulty finding small meeting areas. Perhaps we could let them know it is okay to use private offices as meeting rooms when executives are out of town.

Tips for managing metrics

Here are eight tips to bear in mind as you develop a workplace measurement strategy.

  • Start at the end. Identify your organization’s core business drivers, then work backward to determine what needs to be measured to help achieve desired outcomes. This approach will help you stay focused on metrics that can make a difference.
  • Incorporate multiple points of view. Collect both big and thick data—quantitative and qualitative—to fully understand what is happening in your workplace. Multiple sources of data lead to richer insights. However, data has a cost. Be vigilant against the temptation to collect data just because you can. Collecting data irrelevant to your business goals produces excess noise that makes the signal harder to find.
  • Take the numbers with a grain of salt. Be careful about putting too much stock in quantitative data without adding context with qualitative insights. Decisions made prematurely—before you see the big picture—can be counterproductive and costly.
  • Make space accountable. If data indicates space usage is other than expected, don’t assume you need to nudge people toward a preferred behavior. More likely, the space itself needs to change. Employees should not be held accountable to space. Rather, the space should be accountable to what employees need.
  • Use data to experiment. Data can bring clarity to a proposed workplace solution. Example: A Steelcase client with benching configurations once wondered if installing overhead canopies would make them more appealing. Researchers conducted a small experiment, trying the setup both ways and using sensors to measure occupancy. Result: Canopies clearly increased utilization. Thanks to indisputable data, the client was able to cascade the strategy throughout its operations with confidence. Workplace change should not be based on hunches or trends. Actionable data helps ensure you invest in the right kind of change— the kind that makes efficient use of your real estate investment and better supports employees. It removes the risk of creating a space that doesn’t work as well as it could.
  • Measurement can improve engagement. Qualitative measures like workshops, focus groups, surveys and interviews do more than add insight. They also make employees feel valued. When employees know their opinions matter, they are more likely to keep an open mind about workplace change.
  • Productivity isn’t just about results. Be careful about focusing too narrowly on productivity metrics that you lose sight of what motivates people to be productive in the first place. Leaders need to show they care about the complete employee experience— an inspiring workplace, stimulating professional development, a sense of purpose—not just how proficiently their people check tasks off a to-do list. Striking a balance between accountability and caring ensures employees know what is expected and have a deep desire to succeed.
  • Keep improving. Workplace metrics are not a one-and-done proposition. Continue collecting data to keep abreast of your evolving work experience. Data delivers the most value when used on an ongoing basis to fine-tune your work environment.

Incremental improvement for greater value

Back when workplace metrics rarely ventured beyond square footage, standard operating procedure at most organizations involved major capital investment on a periodic basis. Millions might be spent to overhaul a workplace, which then remained static for a decade or two before the next spend.

In the meantime, of course, the nature of work kept changing. Consequently, the workplace gradually became a barrier, doing an admirable job of meeting employee needs only after each round of big spending.

Today, with higher expectations for the workplace, investments are likely to be more incremental. Organizations can analyze data on an ongoing basis and tweak their work environment as needed. As work changes, the workplace can stay current by responding quickly to the people using it.

The data revolution is making it possible for organizations to make workplace decisions faster, more frequently, and with greater confidence than ever before. By making sense of measurement— by combining big data and thick data—organizations can uncover new ways to realize greater value from corporate real estate.

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